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Downloading...
From (original): https://drive.google.com/uc?id=1gb2vWn0V2unstElGTTrHIIWIftHbXJvz
-From (redirected): https://drive.google.com/uc?id=1gb2vWn0V2unstElGTTrHIIWIftHbXJvz&confirm=t&uuid=fe3c29ed-844d-4549-bcbe-cfcbbb5c4636
+From (redirected): https://drive.google.com/uc?id=1gb2vWn0V2unstElGTTrHIIWIftHbXJvz&confirm=t&uuid=31584bd7-c32c-4a78-a561-6bc179117eca
To: /home/runner/work/fornax-demo-notebooks/fornax-demo-notebooks/light_curves/data/df_lc_020724.parquet.gzip
-100%|██████████| 243M/243M [00:05<00:00, 45.9MB/s]
+100%|██████████| 243M/243M [00:03<00:00, 73.7MB/s]
The left panel is colorcoded by the origin of the sample. The middle panel shows the sum of mean brightnesses in three bands (arbitrary unit) demonstrating that after normalization we see no correlation with brightness. The panel on the right is color coded by a statistical measure of variability (i.e. the fractional variation see here). As with the plotting above it is not easy to see all the data points and correlations in the next two cells measure probability of belonging to each original sample as well as the mean statistical property on an interpolated grid on this reduced 2D projected surface.
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+<matplotlib.colorbar.Colorbar at 0x7fcca8112f50>
-
+
/tmp/ipykernel_1899/3793995111.py:10: RuntimeWarning: invalid value encountered in divide
+/tmp/ipykernel_1931/3793995111.py:10: RuntimeWarning: invalid value encountered in divide
prob = hist_per_cluster / hist
-
+
Figure above shows how with ZTF light curves alone we can separate some of these AGN samples, where they have overlaps. We can do a similar exercise with other dimensionality reduction techniques. Below we show two SOMs one with normalized and another with no normalization. The advantage of Umaps to SOMs is that in practice you may change the parameters to separate classes of vastly different data points, as distance is preserved on a umap. On a SOM however only topology of higher dimensions is preserved and not distance hence, the change on the 2d grid does not need to be smooth and from one cell to next there might be larg jumps. On the other hand, an advantage of the SOM is that by definition it has a grid and no need for a posterior interpolation (as we did above) is needed to map more data or to measure probabilities, etc.
@@ -1040,24 +1040,24 @@Training...
-pca_linear_initialization took: 0.013000 seconds
+pca_linear_initialization took: 0.014000 seconds
Rough training...
radius_ini: 2.000000 , radius_final: 1.000000, trainlen: 5
epoch: 1 ---> elapsed time: 0.042000, quantization error: 7.891746
-epoch: 2 ---> elapsed time: 0.034000, quantization error: 7.278950
-epoch: 3 ---> elapsed time: 0.023000, quantization error: 7.065468
-epoch: 4 ---> elapsed time: 0.016000, quantization error: 6.941469
-epoch: 5 ---> elapsed time: 0.015000, quantization error: 6.804933
+epoch: 2 ---> elapsed time: 0.047000, quantization error: 7.278950
+epoch: 3 ---> elapsed time: 0.017000, quantization error: 7.065468
+epoch: 4 ---> elapsed time: 0.017000, quantization error: 6.941469
+epoch: 5 ---> elapsed time: 0.017000, quantization error: 6.804933
Finetune training...
radius_ini: 1.000000 , radius_final: 1.000000, trainlen: 6
epoch: 1 ---> elapsed time: 0.017000, quantization error: 6.635077
epoch: 2 ---> elapsed time: 0.015000, quantization error: 6.586020
-epoch: 3 ---> elapsed time: 0.015000, quantization error: 6.569531
-epoch: 4 ---> elapsed time: 0.014000, quantization error: 6.555973
-epoch: 5 ---> elapsed time: 0.016000, quantization error: 6.548818
-epoch: 6 ---> elapsed time: 0.017000, quantization error: 6.545620
+epoch: 3 ---> elapsed time: 0.017000, quantization error: 6.569531
+epoch: 4 ---> elapsed time: 0.016000, quantization error: 6.555973
+epoch: 5 ---> elapsed time: 0.017000, quantization error: 6.548818
+epoch: 6 ---> elapsed time: 0.015000, quantization error: 6.545620
Final quantization error: 6.543132
-train took: 0.299000 seconds
+train took: 0.310000 seconds
Training...
-pca_linear_initialization took: 0.079000 seconds
+pca_linear_initialization took: 0.069000 seconds
Rough training...
radius_ini: 2.000000 , radius_final: 1.000000, trainlen: 5
-epoch: 1 ---> elapsed time: 0.288000, quantization error: 5.114529
-epoch: 2 ---> elapsed time: 0.016000, quantization error: 2.607937
-epoch: 3 ---> elapsed time: 0.016000, quantization error: 1.832338
-epoch: 4 ---> elapsed time: 0.015000, quantization error: 1.587327
-epoch: 5 ---> elapsed time: 0.015000, quantization error: 1.480731
+epoch: 1 ---> elapsed time: 0.036000, quantization error: 5.114529
+epoch: 2 ---> elapsed time: 0.034000, quantization error: 2.607937
+epoch: 3 ---> elapsed time: 0.027000, quantization error: 1.832338
+epoch: 4 ---> elapsed time: 0.017000, quantization error: 1.587327
+epoch: 5 ---> elapsed time: 0.024000, quantization error: 1.480731
Finetune training...
radius_ini: 1.000000 , radius_final: 1.000000, trainlen: 6
epoch: 1 ---> elapsed time: 0.015000, quantization error: 1.405660
-epoch: 2 ---> elapsed time: 0.015000, quantization error: 1.394511
-epoch: 3 ---> elapsed time: 0.015000, quantization error: 1.391247
-epoch: 4 ---> elapsed time: 0.015000, quantization error: 1.385959
+epoch: 2 ---> elapsed time: 0.016000, quantization error: 1.394511
+epoch: 3 ---> elapsed time: 0.016000, quantization error: 1.391247
+epoch: 4 ---> elapsed time: 0.017000, quantization error: 1.385959
epoch: 5 ---> elapsed time: 0.016000, quantization error: 1.381555
epoch: 6 ---> elapsed time: 0.016000, quantization error: 1.380592
Final quantization error: 1.377762
-train took: 0.572000 seconds
+train took: 0.358000 seconds
/tmp/ipykernel_1899/3793995111.py:10: RuntimeWarning: invalid value encountered in divide
+/tmp/ipykernel_1931/3793995111.py:10: RuntimeWarning: invalid value encountered in divide
prob = hist_per_cluster / hist
-
+
/tmp/ipykernel_1899/3463858432.py:10: RuntimeWarning: invalid value encountered in divide
+/tmp/ipykernel_1931/3463858432.py:10: RuntimeWarning: invalid value encountered in divide
prob = hist_per_cluster / hist
-
+
(0.5733959794044494,
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-
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Accuracy of Random Interval Classifier: 0.7111111111111111
-CPU times: user 2.09 s, sys: 1.75 s, total: 3.84 s
+Accuracy of Random Interval Classifier: 0.7333333333333333
+CPU times: user 2.18 s, sys: 1.79 s, total: 3.96 s
Wall time: 1min 16s
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ztf
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@@ -1066,7 +1066,7 @@ top
2024/09/27 20:52:36 UTC | starting explode
+2024/09/30 20:54:54 UTC | starting explode
2024/09/27 20:52:37 UTC | [pid=2281] Starting build=sample
+2024/09/30 20:54:54 UTC | [pid=2318] Starting build=sample
Building object sample from: ['yang']
Changing Look AGN- Yang et al: 31
Object sample size, after duplicates removal: 30
Object sample saved to: /home/runner/work/fornax-demo-notebooks/fornax-demo-notebooks/light_curves/output/lightcurves-demo-Yang-sample/object_sample.ecsv
-2024/09/27 20:52:39 UTC
-2024/09/27 20:52:39 UTC | [pid=2330] Starting build=lightcurves, archive=hcv
-2024/09/27 20:52:39 UTC | [pid=2329] Starting build=lightcurves, archive=gaia
-2024/09/27 20:52:39 UTC | [pid=2331] Starting build=lightcurves, archive=heasarc
-2024/09/27 20:52:39 UTC | [pid=2334] Starting build=lightcurves, archive=tess_kepler
-2024/09/27 20:52:39 UTC | [pid=2332] Starting build=lightcurves, archive=icecube
-2024/09/27 20:52:39 UTC | [pid=2335] Starting build=lightcurves, archive=wise
-2024/09/27 20:52:39 UTC | [pid=2333] Starting build=lightcurves, archive=panstarrs
-2024/09/27 20:52:39 UTC | [pid=2336] Starting build=lightcurves, archive=ztf
+2024/09/30 20:54:56 UTC
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+2024/09/30 20:54:56 UTC | [pid=2372] Starting build=lightcurves, archive=ztf
+2024/09/30 20:54:56 UTC | [pid=2371] Starting build=lightcurves, archive=wise
+2024/09/30 20:54:56 UTC | [pid=2369] Starting build=lightcurves, archive=panstarrs
working on mission
FERMIGTRIG
working on mission
@@ -1228,33 +1228,33 @@ Example 2: Parallelizing the light_curve_generator notebook0%| | 0/1 [00:00<?, ?it/s]
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-100%|██████████| 1/1 [00:04<00:00, 4.40s/it]
+100%|██████████| 1/1 [00:03<00:00, 3.32s/it]
+100%|██████████| 1/1 [00:03<00:00, 3.32s/it]
No data found for target "<SkyCoord (ICRS): (ra, dec) in deg
(0.28136, -0.09792)>".
No data found for target "<SkyCoord (ICRS): (ra, dec) in deg
@@ -1317,7 +1317,7 @@ Example 2: Parallelizing the light_curve_generator notebook{"version_major": 2, "version_minor": 0, "model_id": "215911349d224a18ba09ee6e88884c07"}
+
The light curves are saved in the “parquet_dir” directory.
Each archive call writes data to a parquet file in its own subdirectory.
@@ -1370,93 +1370,95 @@
Example 2: Parallelizing the light_curve_generator notebook29
- Yang 18
- BP
- 56959.897476
- 0.145461
- 0.004619
- gaia
-
-
- panstarrs r
- 55352.400562
- 0.073599
- 0.001258
+ 17
+ Yang 18
+ panstarrs i
+ 56794.373880
+ 0.502129
+ 0.003863
panstarrs
- 8
+ 14
Yang 18
- panstarrs y
- 56408.279660
- 0.310015
- 0.009319
- panstarrs
+ G
+ 57739.112160
+ 0.037169
+ 0.001764
+ gaia
- 13
+ 5
Yang 18
- panstarrs g
- 55983.506415
- 0.013669
- 0.001218
- panstarrs
+ BP
+ 57290.020150
+ 0.216419
+ 0.005135
+ gaia
- 21
+ 20
Yang 18
- RP
- 57281.807777
- 0.480648
- 0.010094
+ BP
+ 56900.853961
+ 0.108195
+ 0.007329
gaia
+
+ 6
+ Yang 18
+ panstarrs y
+ 56405.252664
+ 0.123749
+ 0.009207
+ panstarrs
+